{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TFIDF_kmeans_cluster.ipynb",
      "provenance": [],
      "mount_file_id": "18K1rInPXKHmglNLHlNtL30CWcbdfLur5",
      "authorship_tag": "ABX9TyNN2boVztOK+qc0uAGyZ7GO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/monsund/unsupervised-classification-of-Linkedin-Profiles-using-KMeans-LDA-TFIDF/blob/master/TFIDF_kmeans_cluster.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dpkEBwfDgbpp"
      },
      "source": [
        "# **Importing necessary Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2l1lfxh9KgE"
      },
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import csv\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64iao29iBalC"
      },
      "source": [
        "# **Loading Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5XMHFMB9Q-D"
      },
      "source": [
        "input_filepath = '/content/drive/My Drive/NLP/with swaraj/Data'\n",
        "input_filename = 'linkedin_about.csv'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QwlGNp5K9r1y",
        "outputId": "efd1dd97-3588-4d42-daaa-e985a5f8b1e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "df = pd.read_csv(os.path.join(input_filepath,input_filename))\n",
        "df.dropna(inplace=True)\n",
        "df.reset_index(drop=True, inplace=True)  #Reindexing \n",
        "df.head(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>url</th>\n",
              "      <th>username</th>\n",
              "      <th>title</th>\n",
              "      <th>about</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>https://www.linkedin.com/in/lokesh-kumar-xess-...</td>\n",
              "      <td>lokesh-kumar-xess-54814068</td>\n",
              "      <td>Marketing &amp; Strategy at Mobistreak</td>\n",
              "      <td>I am a Google Adwords and Google Analytics cer...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  ...                                              about\n",
              "0           0  ...  I am a Google Adwords and Google Analytics cer...\n",
              "\n",
              "[1 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3QZOMWWX6Bnr"
      },
      "source": [
        "# **Removing Stopwords, Punctuation and Text Lower Case**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3VvG83IU6Fct",
        "outputId": "85437c35-eee3-42f0-dc8d-ab362a751506",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')\n",
        "from nltk.tokenize import word_tokenize"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bDZjoys7D4B"
      },
      "source": [
        "stop_words = set(stopwords.words('english'))\n",
        "punct = set(['.', ',', '&', '\\'', '\"', '(', ')'])\n",
        "stopwords_punct= stop_words.union(punct)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGjxw5t18Vx8"
      },
      "source": [
        "# removing stopwords and punctuation\n",
        "data_without_stopwords_punct = []\n",
        "for i in range(len(df)):\n",
        "  word_tokens = word_tokenize(df.about[i].lower())         #lowering case----------------\n",
        "  filtered_sentence = [w for w in word_tokens if not w in stopwords_punct]\n",
        "  data_without_stopwords_punct.append(' '.join(filtered_sentence))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fahud5zc6aU0",
        "outputId": "5a282d7a-47d3-4143-94f5-8d8988145a1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        }
      },
      "source": [
        "df['cleaned_about_us'] = data_without_stopwords_punct\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>url</th>\n",
              "      <th>username</th>\n",
              "      <th>title</th>\n",
              "      <th>about</th>\n",
              "      <th>cleaned_about_us</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>https://www.linkedin.com/in/lokesh-kumar-xess-...</td>\n",
              "      <td>lokesh-kumar-xess-54814068</td>\n",
              "      <td>Marketing &amp; Strategy at Mobistreak</td>\n",
              "      <td>I am a Google Adwords and Google Analytics cer...</td>\n",
              "      <td>google adwords google analytics certified ppc ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>https://in.linkedin.com/in/sujithnarayanan</td>\n",
              "      <td>sujithnarayanan</td>\n",
              "      <td>Reimagining Financial Services for India</td>\n",
              "      <td>Ideate. Execute. Disrupt. Iterate.</td>\n",
              "      <td>ideate execute disrupt iterate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>https://www.linkedin.com/in/adarshramakrishnan</td>\n",
              "      <td>adarshramakrishnan</td>\n",
              "      <td>Product Strategist | Product Manager | Triple ...</td>\n",
              "      <td>Over the past decade, I have co-founded 3 vent...</td>\n",
              "      <td>past decade co-founded 3 ventures elegant solu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>https://www.linkedin.com/in/rhythm-bhatnagar-4...</td>\n",
              "      <td>rhythm-bhatnagar-4350b551</td>\n",
              "      <td>Product Marketer | Ex-SHEROES | Early-Stage St...</td>\n",
              "      <td>Hey,I am Rhythm. I am passionate about startup...</td>\n",
              "      <td>hey rhythm passionate startups building deeper...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>https://www.linkedin.com/in/karthiksureshlbs</td>\n",
              "      <td>karthiksureshlbs</td>\n",
              "      <td>Product at Facebook | CMU MS | LBS MBA</td>\n",
              "      <td>I am a product-centric builder with experience...</td>\n",
              "      <td>product-centric builder experience founder ear...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  ...                                   cleaned_about_us\n",
              "0           0  ...  google adwords google analytics certified ppc ...\n",
              "1           1  ...                     ideate execute disrupt iterate\n",
              "2           2  ...  past decade co-founded 3 ventures elegant solu...\n",
              "3           3  ...  hey rhythm passionate startups building deeper...\n",
              "4           4  ...  product-centric builder experience founder ear...\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vbM23U_jMoka"
      },
      "source": [
        "# **Vectorize data with TFIDF**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wjnplSigMvGZ"
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnsJ00bLNOdy",
        "outputId": "e0efdab1-34f2-4b13-c2b3-d5b783110eb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=5, ngram_range=(1,2) , max_features=15000)\n",
        "X = tfidf_vectorizer.fit_transform(df.cleaned_about_us.to_list())\n",
        "model = X.toarray()\n",
        "print(len(model), len(model[0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9541 15000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACTirFbTj11S"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components=2).fit(model)\n",
        "datapoint = pca.transform(model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "skAFD_Q7TP-O"
      },
      "source": [
        "# **Clustering**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oNCQeQ8BTS0U"
      },
      "source": [
        "from sklearn.cluster import KMeans"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1SdVlRDuTTqo"
      },
      "source": [
        "kmeans_model = KMeans(n_clusters=4, init='k-means++', max_iter=100) \n",
        "# X = kmeans_model.fit(model_pvdm.docvecs.doctag_syn0)\n",
        "X = kmeans_model.fit(model)\n",
        "labels = kmeans_model.labels_.tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfTAS2QPTtKY"
      },
      "source": [
        "kmeans_prediction = kmeans_model.fit_predict(model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pFNwtqbjlfoS"
      },
      "source": [
        "# **Elbow method for finding optimal K in Kmeans**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zPwrD-gmlol5"
      },
      "source": [
        "# function returns Within-Cluster-Sum of Squared Errors (WSS) score for k values from 1 to kmax\n",
        "def calculate_WSS(points, kmax):\n",
        "  sse = []\n",
        "  for k in range(1, kmax+1):\n",
        "    kmeans = KMeans(n_clusters = k).fit(points)\n",
        "    centroids = kmeans.cluster_centers_\n",
        "    pred_clusters = kmeans.predict(points)\n",
        "    curr_sse = 0\n",
        "    \n",
        "    # calculate square of Euclidean distance of each point from its cluster center and add to current WSS\n",
        "    for i in range(len(points)):\n",
        "      curr_center = centroids[pred_clusters[i]]\n",
        "      curr_sse += (points[i, 0] - curr_center[0]) ** 2 + (points[i, 1] - curr_center[1]) ** 2\n",
        "      \n",
        "    sse.append(curr_sse)\n",
        "  return sse"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zb1JN6BClypq"
      },
      "source": [
        "k_size = 10\n",
        "wss = calculate_WSS(datapoint, k_size)\n",
        "k = [x for x in range(k_size)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1n5LXfeumTM3",
        "outputId": "8f0ea582-b785-4ef7-8768-75b75e3a76fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "source": [
        "plt.figure(figsize=(20,10)) # Size of figure is adjusted.\n",
        "plt.xticks(fontsize=20) # Size of number labels on x-axis is adjusted.\n",
        "plt.yticks(fontsize=20) # Size of number labels on y-axis is adjusted.\n",
        "plt.plot(k,wss,'go--', linewidth=1.5, markersize=4) # Graph is plotted.\n",
        "plt.xlabel('Value of k',fontsize = 25) # x-axis is labelled.\n",
        "plt.ylabel('Value of WSS',fontsize = 25) # y-axis is labelled."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Value of WSS')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PyHG6vCLnXnr"
      },
      "source": [
        "# ***The Silhouette Method ***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zoTeEpljnx34"
      },
      "source": [
        "from sklearn.metrics import silhouette_score\n",
        "\n",
        "sil = []\n",
        "kmax = 20\n",
        "\n",
        "# dissimilarity would not be defined for a single cluster, thus, minimum number of clusters should be 2\n",
        "for k in range(2, kmax+1):\n",
        "  kmeans = KMeans(n_clusters = k).fit(datapoint)\n",
        "  labels = kmeans.labels_\n",
        "  sil.append(silhouette_score(datapoint, labels, metric = 'euclidean'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1wEgIJVfotMD"
      },
      "source": [
        "k_sil = [x for x in range(2,21)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pccUwQwcofdG",
        "outputId": "26079c66-f762-40f6-88af-2fcd66de84a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        }
      },
      "source": [
        "plt.figure(figsize=(20,10)) # Size of figure is adjusted.\n",
        "plt.xticks(fontsize=20) # Size of number labels on x-axis is adjusted.\n",
        "plt.yticks(fontsize=20) # Size of number labels on y-axis is adjusted.\n",
        "plt.plot(k_sil,sil,'go--', linewidth=1.5, markersize=4) # Graph is plotted.\n",
        "plt.xlabel('Value of k',fontsize = 25) # x-axis is labelled.\n",
        "plt.ylabel('Value of sil',fontsize = 25) # y-axis is labelled."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Value of sil')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5O9N8w-AltIR"
      },
      "source": [
        "# **Visualization of Clusters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uDOXGChkT6qD",
        "outputId": "f0b389f1-4724-474c-a7ab-03aa069db0cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.figure(figsize=(10, 5))\n",
        "# label1 = ['#FFFF00', '#008000', '#0000FF', '#800080', '#800450', '#800FF0', '#112328', '#620000', '#FF00FF', 'orange','#00FF66', '#302B54', '#CDAD00', '#D0FAEE', '#D41A1F']\n",
        "label1 =['red', 'green', 'yellow', 'blue']\n",
        "color = [label1[i] for i in labels]\n",
        "plt.scatter(datapoint[:, 0], datapoint[:, 1], c=color)\n",
        "centroids = kmeans_model.cluster_centers_\n",
        "centroidpoint = pca.transform(centroids)\n",
        "plt.scatter(centroidpoint[:, 0], centroidpoint[:, 1], marker='^', s=150, c='#000000')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zAUWc7AiYDhU",
        "outputId": "c7042c94-bde6-4195-d241-6f7e7c0eb332",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df[\"cluster\"] = kmeans_prediction\n",
        "\n",
        "df[df[\"cluster\"]==1].head(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>url</th>\n",
              "      <th>username</th>\n",
              "      <th>title</th>\n",
              "      <th>about</th>\n",
              "      <th>cleaned_about_us</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>https://www.linkedin.com/in/lokesh-kumar-xess-...</td>\n",
              "      <td>lokesh-kumar-xess-54814068</td>\n",
              "      <td>Marketing &amp; Strategy at Mobistreak</td>\n",
              "      <td>I am a Google Adwords and Google Analytics cer...</td>\n",
              "      <td>google adwords google analytics certified ppc ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>https://www.linkedin.com/in/karthikprabhakar</td>\n",
              "      <td>karthikprabhakar</td>\n",
              "      <td>Executive Director and Partner at Chiratae Ven...</td>\n",
              "      <td>Karthik Prabhakar is the Executive Director an...</td>\n",
              "      <td>karthik prabhakar executive director partner c...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10</td>\n",
              "      <td>https://www.linkedin.com/in/supadhyay</td>\n",
              "      <td>supadhyay</td>\n",
              "      <td>CEO/GM for AI/SaaS/Data companies</td>\n",
              "      <td>Product and GTM for companies at the intersect...</td>\n",
              "      <td>product gtm companies intersection data aispec...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>13</td>\n",
              "      <td>https://www.linkedin.com/in/leandromargulis</td>\n",
              "      <td>leandromargulis</td>\n",
              "      <td>Business Accelerator | People Connector | Advo...</td>\n",
              "      <td>Entrepreneurial leader with strong Business De...</td>\n",
              "      <td>entrepreneurial leader strong business develop...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>14</td>\n",
              "      <td>https://www.linkedin.com/in/jphillipnelson</td>\n",
              "      <td>jphillipnelson</td>\n",
              "      <td>Product Manager at Google</td>\n",
              "      <td>I am an experienced entrepreneur, product lead...</td>\n",
              "      <td>experienced entrepreneur product leader softwa...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>17</td>\n",
              "      <td>https://www.linkedin.com/in/jatinmehtaaiqudo</td>\n",
              "      <td>jatinmehtaaiqudo</td>\n",
              "      <td>VP, Business Development at Aiqudo (Previously...</td>\n",
              "      <td>A senior leader with 15 years of experience in...</td>\n",
              "      <td>senior leader 15 years experience business dev...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>https://www.linkedin.com/in/brianli2004</td>\n",
              "      <td>brianli2004</td>\n",
              "      <td>Bizops/CorpDev. Ex-Linkedin. Moved to LA.</td>\n",
              "      <td>Having fun building teams and operations at hi...</td>\n",
              "      <td>fun building teams operations high-growth tech...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21</td>\n",
              "      <td>https://www.linkedin.com/in/shravansogani</td>\n",
              "      <td>shravansogani</td>\n",
              "      <td>Senior Director Of Engineering at SurveyMonkey</td>\n",
              "      <td>Passionate, well-grounded, and decisive Engine...</td>\n",
              "      <td>passionate well-grounded decisive engineering ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>https://in.linkedin.com/in/nshntdxt</td>\n",
              "      <td>nshntdxt</td>\n",
              "      <td>Engineering Leader at Slack</td>\n",
              "      <td>Building high functioning agile teams, one ste...</td>\n",
              "      <td>building high functioning agile teams one step...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>25</td>\n",
              "      <td>https://www.linkedin.com/in/rsudhirshenoy</td>\n",
              "      <td>rsudhirshenoy</td>\n",
              "      <td>Product Management, Marketing and Channel Mana...</td>\n",
              "      <td>Experienced Professional in the Technology Ind...</td>\n",
              "      <td>experienced professional technology industry r...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>29</td>\n",
              "      <td>https://www.linkedin.com/in/talvindersingh</td>\n",
              "      <td>talvindersingh</td>\n",
              "      <td>pragmaticleaders.io - Become a Product Manager...</td>\n",
              "      <td>A committed and highly focused entrepreneur an...</td>\n",
              "      <td>committed highly focused entrepreneur product ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>33</td>\n",
              "      <td>https://www.linkedin.com/in/atingarg29</td>\n",
              "      <td>atingarg29</td>\n",
              "      <td>PM at Telio</td>\n",
              "      <td>Love solving product-journey problems in the c...</td>\n",
              "      <td>love solving product-journey problems consumer...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>39</td>\n",
              "      <td>https://in.linkedin.com/in/deepak-gupta-92655a12</td>\n",
              "      <td>deepak-gupta-92655a12</td>\n",
              "      <td>CFO at Indo Nissin Foods Pvt. Ltd</td>\n",
              "      <td>Finance Professional with over 21+ years of ri...</td>\n",
              "      <td>finance professional 21+ years rich experience...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>40</td>\n",
              "      <td>https://www.linkedin.com/in/deepa-wasson-29b58...</td>\n",
              "      <td>deepa-wasson-29b58a95</td>\n",
              "      <td>CFO at Indo Nissin Foods Pvt. Ltd</td>\n",
              "      <td>Finance Professional with over 21+ years of ri...</td>\n",
              "      <td>finance professional 21+ years rich experience...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>41</td>\n",
              "      <td>https://in.linkedin.com/in/rishi-srivastava-12...</td>\n",
              "      <td>rishi-srivastava-120b005</td>\n",
              "      <td>Founder AARK TechAccess, General Partner BOV C...</td>\n",
              "      <td>Executive with 26+ years of international expe...</td>\n",
              "      <td>executive 26+ years international experience t...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>42</td>\n",
              "      <td>https://www.linkedin.com/in/erincummins</td>\n",
              "      <td>erincummins</td>\n",
              "      <td>Director of Product at Artemis</td>\n",
              "      <td>Product manager/leader and startup advisor wit...</td>\n",
              "      <td>product manager/leader startup advisor nine ye...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>43</td>\n",
              "      <td>https://in.linkedin.com/in/anjali-tiwary</td>\n",
              "      <td>anjali-tiwary</td>\n",
              "      <td>Founder &amp; CEO, AugLi</td>\n",
              "      <td>Well communicated ideas hold the power to infl...</td>\n",
              "      <td>well communicated ideas hold power influence p...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>55</td>\n",
              "      <td>https://www.linkedin.com/in/mohammedaboobacker</td>\n",
              "      <td>mohammedaboobacker</td>\n",
              "      <td>YCombinator Alum | Ex Freshworks | 2X Entrepre...</td>\n",
              "      <td>Energetic self-starter and continuous learner ...</td>\n",
              "      <td>energetic self-starter continuous learner focu...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>63</td>\n",
              "      <td>https://www.linkedin.com/in/ujjwaltrivedi</td>\n",
              "      <td>ujjwaltrivedi</td>\n",
              "      <td>Products at MoveInSync</td>\n",
              "      <td>Currently, leading the SaaS Product team that ...</td>\n",
              "      <td>currently leading saas product team manages 3 ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>66</td>\n",
              "      <td>https://www.linkedin.com/in/sandeeptodi</td>\n",
              "      <td>sandeeptodi</td>\n",
              "      <td>Co-founder &amp; Chief Business Officer at REMITR....</td>\n",
              "      <td>International payments technology is age-old a...</td>\n",
              "      <td>international payments technology age-old brok...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Unnamed: 0  ... cluster\n",
              "0            0  ...       1\n",
              "6            6  ...       1\n",
              "10          10  ...       1\n",
              "13          13  ...       1\n",
              "14          14  ...       1\n",
              "17          17  ...       1\n",
              "19          20  ...       1\n",
              "20          21  ...       1\n",
              "21          22  ...       1\n",
              "23          25  ...       1\n",
              "26          29  ...       1\n",
              "28          33  ...       1\n",
              "30          39  ...       1\n",
              "31          40  ...       1\n",
              "32          41  ...       1\n",
              "33          42  ...       1\n",
              "34          43  ...       1\n",
              "41          55  ...       1\n",
              "47          63  ...       1\n",
              "49          66  ...       1\n",
              "\n",
              "[20 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d7mrOJJDnRL1",
        "outputId": "d89b5a07-2bc9-445b-fb49-5816b5d7f310",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "print(len(df[df['cluster']==0]))\n",
        "df[df[\"cluster\"]==0].about.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "171\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "310     Socio en EntrepreneurlandSocio en FairwindSoci...\n",
              "923     Problemen oplossen op snijvlak Business - IT, ...\n",
              "1255    Emprendedor & Programador | Cofundador de Cole...\n",
              "1457    Co-fundador de Rocketbot, solución de RPA que ...\n",
              "1480    Antti Kulppi es experto en tecnología y nuevas...\n",
              "1482    En los últimos años he incubado 5 proyectos es...\n",
              "1483     En los últimos años he incubado 5 proyectos e...\n",
              "1557    Building Open Banking en FloidInformación fina...\n",
              "2478    Orientación y Atención en la Estrategia y Ases...\n",
              "2907    🔥 DEUX.IO ➭• La meilleure agence de Growth Mar...\n",
              "Name: about, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hJjX5o1cntu"
      },
      "source": [
        "df.to_csv('/content/drive/My Drive/NLP/with swaraj/Data/tfidf_kmeans_cluster_results_123.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rSrJwEM1ZqcQ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}